In 1843, The Economist’s inaugural edition went to print with a table front and centre. Clearly ahead of his time, the editor of the day recognised the power of data journalism over a 100 years before the field’s modern emergence.

Almost 176 years later and the outlet’s appetite for data driven stories is still going strong. In 2015, they brought in a specialised data team and, this year, they launched a dedicated data section in print.

To find out more about The Economist’s affinity with data, we let you pose your burning questions to the data team themselves. Here’s what they had to say!

1858, Florence Nightingale published a study on the conditions of army hospitals, her seminal Notes on Matters Affecting the Health, Efficiency, and Hospital Administration of the British Army. Her Diagram of the Causes of Mortality had a singular goal: to vividly demonstrate that the lack of proper sanitary caretaking facilities was a far more severe, but also far more avoidable, cause of death for soldiers than injuries suffered in battle. It’s one thing to simply state that the disease killed a lot of soldiers. It’s another thing entirely to effectively and actionably juxtapose it against the casualties encountered at the hands of the opposing army.

you conduct a quick internet search on “history of data visualization,” you’ll nearly always see Florence Nightingale included in the annals of history. Why? It’s not like a Nightingale Rose chart is easy to read, or a cinch to make, or even all that common.

Data journalism is a field closely related with data science. To write an article, data journalists have to follow the traditional steps of any data driven project. These include exploratory and explanatory analysis, and data visualization is a key step in both.

During exploratory analysis, journalists must be able to quickly understand their data through simple graphics, going quickly from one chart to another to answer their questions. Once interesting results are discovered, data visualization is often used to showcase these results. But for a story to be eye-catching and easy to understand, the journalist will often spend a lot of time customizing the graphic. .

Tech giant Microsoft recently announced a ton of new features that it would be adding to our old pal Excel. Perhaps its time and God knows Excel has waited long enough for a major upgrade. But what will this upgrade actually do? Will it really live up to the buzz its announcement stirred up? And what are these new data types that they are talking about? We will try to answer all these queries here. Let’s dive in !

“It is meant to take any list of data and then start to generate insights”. Spataro [Microsoft’s general manager for Office] also said, “It will look at combinations, charts, pivot tables and it will recognize those that are most interesting by looking at outliers, looking at trends in the data, looking at things that represent changes.” It is named #INSIGHTS as of now. And machine learning is also being incorporated into this in order to facilitate the ability to take data from other services using APIs.

Anyone who recently bought an exploding smartphone or spent hours sleeping on the floor at Heathrow’s Terminal 5 might be inclined to agree with American inventor Danny Hillis’s definition of technology as “everything that doesn’t work yet”.

As a society, we continue to be obsessed with the latest technology. And as data visualisation enthusiasts, we continue to be seduced by the latest tools, rarely questioning whether novelty leads to better results.

#visualisation#représentation#Statistiques#datavisualisationReaders often ask me what software we use to make charts at the Financial Times. No chart has generated more questions of this type recently than the Sankey diagrams, which we have used frequently this year to explain shifts in voting patterns in elections across Europe.

remember Kenneth Arrow? The infamous mathematician who founded the study of voting systems in the 1950’s? Well, in an interview 60 years later, Kenneth Arrow had this to say, about which voting method he likes most now:

“Well, I’m a little inclined to think that score systems [like Approval & Score Voting] where you categorize in maybe three or four classes [so, giving a score out of 3 or 4, not 10 or 100] probably – in spite of what I said about manipulation [strategic voting] – is probably the best.”

Welcome to the Massive Open Online Course (MOOC) “Data Exploration and Storytelling: Finding Stories in Data with Exploratory Analysis and Visualization,” offered by the Knight Center for Journalism in the Americas at the University of Texas at Austin. This is a free course open to anyone from anywhere in the world interested in data-journalism. Instructors Alberto Cairo and Heather Krause will teach how to extract journalistic stories from data using visualization, exploratory data analysis and other techniques. Learn more details below about this program and if you have any questions, please contact us at knightcenter@austin.utexas.edu.

And we can see this with the most recent Google Trends Freaking Outrage (GTFO), like this Washington Post story titled “The British are frantically Googling what the E.U. is, hours after voting to leave it.”

They note that searches about the EU tripled. But how many people is that? Are they voters? Are they eligible to vote? Were they Leave or Remain? Trends doesn’t tell us, all it does is give us a nice graph with a huge peak. More likely, it’s a very small number of people, based on this graph that puts it in context with other searches in the region:

[...]

But it’s giving plenty of people cover to insult the entire country, when it’s likely just a few people searching for something in a way that they always search for something. It makes “The British are frantically Googling what the EU is, hours after voting to leave it” absurdly disingenuous without better numbers. Remy Smith points this out: The peak was merely ~1000 people! It’s ludicrous that so few people get turned into a massive story, but it underscores the need for context.

Once we’ve cleaned our data, we’re left with a brand new problem: how can we (and others!) verify that what we’ve done is correct and that we haven’t corrupted the data by making these changes? After all, the processed data may look vastly different from the raw data we started out with.